Who is getting the public goods in India: Some evidence
Bibliographic record
Abstract
The way you grow up in India, it has long been known, depends on where you grow up. The average child growing up in Orissa in the 1980s was seven times more likely to die in infancy than his or her equivalent in Kerala. 2 His or her mother is four and half times more likely to die in giving birth if she were in Assam than she would be had she been in Kerala. 3 And if she happens to be a girl and born in Rajasthan in the 1980s, the likelihood of her being literate by the time she was 14 was about a quarter of what it would have been had she grown up in Kerala. 4 This is, as Dreze and Sen (1995), among others, have argued is entirely what we might have expected: In 1991, rural Kerala had 17 times as many hospital beds per head as Orissa and 10 times as many as Assam. The fraction of people in rural Orissa with access to medical facilities in their village in 1981 was less than 11 % compared to 96 % in Kerala. In 1991, 93 % of villages in Kerala had a middle school but the corresponding fraction in Orissa and Assam was less than 25 % and in UP it was less than 15%. What is less often emphasized but equally striking is the extent of variation within a single state: According to the 1991 census, less than 7 % of the villages in Vishakhapatnam district in Andhra Pradesh had middle schools and just over 46 % had some educational facility, as against 55 % and 100 % in Guntur. The district of Rangareddy had only 6 % of villages with primary health sub-centers as against almost 40 % in Anantapur. Less than 1 % of villages in Vishakhapatnam had tapped water compared to 59 % in West Godavari. Forty-eight percent of villages in Vishakhapatnam were using electrical power as against essentially 100 % in Krishna. Twenty percent of 1 I am grateful to Pranab Bardhan, Kaushik Basu and Maitreesh Ghatak for helpful comments. I also wish to thank, without implicating in any way, Lakshmi Iyer and Rohini Somanathan for their ongoing collaboration in the research that lies behind this paper. 2 Based on the 1991 census.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".